{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.5850456 , 4.10541558],\n",
       "       [4.11364445, 2.96710146],\n",
       "       [2.33449241, 3.33233069],\n",
       "       [0.04803803, 3.63787434],\n",
       "       [3.79839492, 3.39760194],\n",
       "       [2.06138849, 2.10822492],\n",
       "       [0.56426802, 1.69261604],\n",
       "       [1.83191072, 1.60379859],\n",
       "       [0.41735535, 1.45316179],\n",
       "       [2.3636583 , 1.25499073],\n",
       "       [2.28830632, 4.35466496],\n",
       "       [4.87521781, 3.67424919],\n",
       "       [3.63312906, 1.43730427],\n",
       "       [3.28512254, 3.09127154],\n",
       "       [3.64053263, 4.49646492],\n",
       "       [1.88696228, 2.01268788],\n",
       "       [4.39852308, 3.07218313],\n",
       "       [0.05095786, 2.38610889],\n",
       "       [2.16879486, 4.0729915 ],\n",
       "       [2.06377118, 4.05313696],\n",
       "       [1.88963106, 2.10528212],\n",
       "       [1.61095712, 4.91345452],\n",
       "       [0.8602311 , 2.98217923],\n",
       "       [3.61718441, 3.56767994],\n",
       "       [3.83643153, 1.92579652],\n",
       "       [2.84969337, 2.15572651],\n",
       "       [0.06754122, 1.39260207],\n",
       "       [4.83128284, 0.31033055],\n",
       "       [2.2504691 , 0.89476527],\n",
       "       [2.95916127, 3.85283408],\n",
       "       [3.41500697, 1.85443348],\n",
       "       [4.07370406, 4.18606542],\n",
       "       [0.40178875, 4.93284397],\n",
       "       [0.16416349, 3.86356726],\n",
       "       [1.49665787, 0.49233594],\n",
       "       [4.18586625, 0.88449765],\n",
       "       [0.46612184, 2.31176906],\n",
       "       [2.69237722, 4.55443633],\n",
       "       [1.39522462, 1.23841702],\n",
       "       [4.56364734, 2.46359623],\n",
       "       [1.0994197 , 3.42953009],\n",
       "       [1.5972092 , 3.75842513],\n",
       "       [4.2196655 , 2.81618315],\n",
       "       [0.88688803, 2.7943508 ],\n",
       "       [4.31871154, 4.1893591 ],\n",
       "       [4.82806675, 0.78178138],\n",
       "       [0.74432966, 2.04213877],\n",
       "       [2.71483134, 3.44184959],\n",
       "       [4.9107349 , 0.87654211],\n",
       "       [0.68165493, 0.75069021],\n",
       "       [0.98753039, 4.70774771],\n",
       "       [2.80726259, 4.91450657],\n",
       "       [4.76222584, 2.4834761 ],\n",
       "       [4.82973533, 0.26616005],\n",
       "       [1.64942518, 0.52382805],\n",
       "       [2.07951304, 0.10183496],\n",
       "       [4.7024911 , 3.89964449],\n",
       "       [1.35905815, 3.71259182],\n",
       "       [4.56439675, 1.47557013],\n",
       "       [3.64593544, 2.9346761 ],\n",
       "       [1.20408242, 1.42553657],\n",
       "       [3.26823867, 3.75562407],\n",
       "       [1.77727078, 4.87512273],\n",
       "       [2.19547507, 3.35056936],\n",
       "       [2.62767125, 4.54351765],\n",
       "       [4.1731174 , 0.72766653],\n",
       "       [4.14180259, 3.73228564],\n",
       "       [3.25084463, 3.48990238],\n",
       "       [3.170528  , 4.37318557],\n",
       "       [4.09537892, 4.79766486],\n",
       "       [3.03669169, 4.01164349],\n",
       "       [4.50511491, 0.66443323],\n",
       "       [1.82924782, 2.75126693],\n",
       "       [3.29632874, 4.3990523 ],\n",
       "       [2.87692166, 1.91098066],\n",
       "       [0.81195447, 1.05363516],\n",
       "       [1.00775655, 1.05097831],\n",
       "       [2.95737062, 2.60502404],\n",
       "       [2.57373257, 2.6080246 ],\n",
       "       [4.51829484, 1.32475835],\n",
       "       [4.00427247, 2.93809702],\n",
       "       [0.98984823, 3.22212986],\n",
       "       [0.76906444, 4.36129376],\n",
       "       [0.6997849 , 3.78588706],\n",
       "       [1.21913868, 3.40266956],\n",
       "       [0.86440811, 3.32461482],\n",
       "       [0.03282835, 2.82236104],\n",
       "       [4.36114904, 1.14002566],\n",
       "       [4.87438483, 2.78056202],\n",
       "       [4.98955005, 1.61023965],\n",
       "       [0.6778257 , 1.98640225],\n",
       "       [0.62118899, 4.01789837],\n",
       "       [4.7799201 , 3.52364015],\n",
       "       [3.91590066, 4.28709893],\n",
       "       [2.65270566, 0.82498262],\n",
       "       [2.87812413, 0.16161534],\n",
       "       [4.22117126, 2.14636603],\n",
       "       [3.98034535, 4.75080549],\n",
       "       [1.66230631, 0.58739126],\n",
       "       [0.47923087, 1.21952   ]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#生成第一种类型点\n",
    "array1 = np.random.uniform(0.0,5,size=(100,2)) #从均匀分布中随机采样\n",
    "array1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.14337639, 6.81981084],\n",
       "       [5.08557057, 7.62704995],\n",
       "       [4.53875589, 9.84954951],\n",
       "       [7.52246096, 6.84500206],\n",
       "       [5.38697613, 9.36720982],\n",
       "       [8.74372155, 6.04910014],\n",
       "       [6.59804282, 9.75462209],\n",
       "       [6.27566797, 5.57243119],\n",
       "       [4.99657509, 9.88267949],\n",
       "       [9.88099553, 5.76150976],\n",
       "       [8.17380378, 8.35766118],\n",
       "       [7.76412097, 4.4181318 ],\n",
       "       [6.14390766, 4.89791554],\n",
       "       [6.91357929, 9.9633737 ],\n",
       "       [8.48155409, 4.58040692],\n",
       "       [9.96455012, 7.7026289 ],\n",
       "       [4.8257575 , 8.96271903],\n",
       "       [9.77670156, 9.43761066],\n",
       "       [9.21350703, 6.91901191],\n",
       "       [7.36337541, 4.85442551],\n",
       "       [4.2214597 , 6.74220548],\n",
       "       [9.94123502, 8.41209003],\n",
       "       [7.42213375, 5.45081011],\n",
       "       [5.32382036, 6.9065182 ],\n",
       "       [9.52285066, 6.59787746],\n",
       "       [6.2023619 , 5.72059086],\n",
       "       [4.63379336, 7.42030856],\n",
       "       [5.71684895, 4.64067522],\n",
       "       [8.77559034, 4.43812498],\n",
       "       [6.30666804, 6.37192391],\n",
       "       [8.21954045, 6.99877084],\n",
       "       [9.27425163, 8.31846276],\n",
       "       [8.13807106, 8.80835878],\n",
       "       [4.63577491, 8.13477423],\n",
       "       [6.65615871, 9.0617186 ],\n",
       "       [5.18410886, 7.60173739],\n",
       "       [5.70553977, 5.73916941],\n",
       "       [6.66112323, 4.51726118],\n",
       "       [5.9932493 , 4.96403124],\n",
       "       [6.50373781, 4.36558554],\n",
       "       [6.22165637, 5.8231623 ],\n",
       "       [5.84852151, 4.36688832],\n",
       "       [5.34623487, 9.43840135],\n",
       "       [7.05255964, 5.98232568],\n",
       "       [8.78449707, 9.98581306],\n",
       "       [6.71395122, 8.23960708],\n",
       "       [8.71170962, 9.69677427],\n",
       "       [6.05164593, 7.14795232],\n",
       "       [9.64521141, 9.55164759],\n",
       "       [6.90868929, 8.4520973 ],\n",
       "       [9.36371906, 5.10572502],\n",
       "       [5.13917674, 5.89065725],\n",
       "       [9.46189129, 5.06986672],\n",
       "       [9.39860682, 7.81581939],\n",
       "       [9.22424428, 5.15320247],\n",
       "       [8.48684076, 5.36308751],\n",
       "       [9.20978244, 9.1503132 ],\n",
       "       [8.25527645, 6.44778976],\n",
       "       [5.3100285 , 6.66130708],\n",
       "       [7.22391841, 6.74299902],\n",
       "       [5.41710502, 7.72479612],\n",
       "       [4.23390659, 7.50179503],\n",
       "       [7.55273111, 6.7291024 ],\n",
       "       [6.67808096, 8.61136136],\n",
       "       [7.39649633, 5.33378976],\n",
       "       [7.35745782, 5.28006254],\n",
       "       [4.79195264, 4.47660412],\n",
       "       [9.75361153, 7.38030536],\n",
       "       [6.4719487 , 9.26361465],\n",
       "       [5.23596703, 6.54422712],\n",
       "       [6.26978361, 6.40366814],\n",
       "       [8.6211076 , 4.40392954],\n",
       "       [5.04520501, 7.91888045],\n",
       "       [5.93059818, 5.58810982],\n",
       "       [6.73383778, 8.81281657],\n",
       "       [9.70224182, 9.77380232],\n",
       "       [6.06751548, 8.42861864],\n",
       "       [8.80245505, 4.80098645],\n",
       "       [4.75253059, 9.39366623],\n",
       "       [7.68598188, 8.59690807],\n",
       "       [7.10492676, 4.88879495],\n",
       "       [5.51095097, 5.14806173],\n",
       "       [7.72021794, 9.57115829],\n",
       "       [7.37329511, 6.64570086],\n",
       "       [4.62218062, 9.78034156],\n",
       "       [6.3686278 , 7.73917796],\n",
       "       [8.18073625, 5.58946285],\n",
       "       [5.7138797 , 4.71747087],\n",
       "       [9.15004884, 6.05036515],\n",
       "       [6.64575875, 4.48032579],\n",
       "       [9.76553812, 7.90592096],\n",
       "       [5.69952663, 9.2283176 ],\n",
       "       [6.94245455, 6.62398946],\n",
       "       [9.69035475, 6.82773576],\n",
       "       [4.77442311, 7.63073413],\n",
       "       [5.97928997, 8.21464459],\n",
       "       [7.5423845 , 5.55696379],\n",
       "       [5.47651187, 7.3306161 ],\n",
       "       [6.82651236, 7.57808183],\n",
       "       [4.71667185, 5.77237869]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#生成第二种类型点\n",
    "array2 = np.random.uniform(4.2,10,size=(100,2)) #从均匀分布中随机采样\n",
    "array2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x243d6cf3108>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.savetxt('./matplot_text_data1.txt',array1,fmt=\"%f %f\",delimiter='\\n')\n",
    "np.savetxt('./matplot_text_data2.txt',array2,fmt=\"%f %f\",delimiter='\\n')\n",
    "a=np.loadtxt('./matplot_text_data1.txt')\n",
    "b=np.loadtxt('./matplot_text_data2.txt')\n",
    "plt.scatter(a[:,0],a[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x243d6da46c8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(a[:,0],a[:,1])\n",
    "plt.scatter(b[:,0],b[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x243e1f2cb08>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[1,2,3,4]\n",
    "y=[2,3,4,8]\n",
    "plt.scatter(x,y,s=[20,40,80,300],color=['r','g','b','y'],marker=\"h\")#color='#ff0000'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([166.1626436 , 172.02685916, 188.00236405, 171.3714187 ,\n",
       "       171.01564533, 186.95387017, 159.11322664, 167.00945137,\n",
       "       171.53605157, 199.75500141, 167.26511138, 140.07715282,\n",
       "       148.22223803, 164.46384483, 188.09165811, 155.74431424,\n",
       "       159.50046284, 183.62316487, 161.62502985, 146.71965895])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_high = np.random.uniform(140,200,size=20)\n",
    "male_high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_id=np.arange(21)\n",
    "male_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_id=np.delete(male_id,0)\n",
    "male_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[166.1626436 ],\n",
       "       [172.02685916],\n",
       "       [188.00236405],\n",
       "       [171.3714187 ],\n",
       "       [171.01564533],\n",
       "       [186.95387017],\n",
       "       [159.11322664],\n",
       "       [167.00945137],\n",
       "       [171.53605157],\n",
       "       [199.75500141],\n",
       "       [167.26511138],\n",
       "       [140.07715282],\n",
       "       [148.22223803],\n",
       "       [164.46384483],\n",
       "       [188.09165811],\n",
       "       [155.74431424],\n",
       "       [159.50046284],\n",
       "       [183.62316487],\n",
       "       [161.62502985],\n",
       "       [146.71965895]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_high=male_high.reshape(20,1)\n",
    "male_high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1],\n",
       "       [ 2],\n",
       "       [ 3],\n",
       "       [ 4],\n",
       "       [ 5],\n",
       "       [ 6],\n",
       "       [ 7],\n",
       "       [ 8],\n",
       "       [ 9],\n",
       "       [10],\n",
       "       [11],\n",
       "       [12],\n",
       "       [13],\n",
       "       [14],\n",
       "       [15],\n",
       "       [16],\n",
       "       [17],\n",
       "       [18],\n",
       "       [19],\n",
       "       [20]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_id=male_id.reshape(20,-1)\n",
    "male_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.        , 166.1626436 ],\n",
       "       [  2.        , 172.02685916],\n",
       "       [  3.        , 188.00236405],\n",
       "       [  4.        , 171.3714187 ],\n",
       "       [  5.        , 171.01564533],\n",
       "       [  6.        , 186.95387017],\n",
       "       [  7.        , 159.11322664],\n",
       "       [  8.        , 167.00945137],\n",
       "       [  9.        , 171.53605157],\n",
       "       [ 10.        , 199.75500141],\n",
       "       [ 11.        , 167.26511138],\n",
       "       [ 12.        , 140.07715282],\n",
       "       [ 13.        , 148.22223803],\n",
       "       [ 14.        , 164.46384483],\n",
       "       [ 15.        , 188.09165811],\n",
       "       [ 16.        , 155.74431424],\n",
       "       [ 17.        , 159.50046284],\n",
       "       [ 18.        , 183.62316487],\n",
       "       [ 19.        , 161.62502985],\n",
       "       [ 20.        , 146.71965895]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male=np.concatenate((male_id,male_high),axis=1)\n",
    "male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.        , 146.4799221 ],\n",
       "       [  2.        , 196.46630789],\n",
       "       [  3.        , 179.16361409],\n",
       "       [  4.        , 191.11168056],\n",
       "       [  5.        , 169.75145576],\n",
       "       [  6.        , 142.19080864],\n",
       "       [  7.        , 171.1102571 ],\n",
       "       [  8.        , 176.4684961 ],\n",
       "       [  9.        , 156.94563434],\n",
       "       [ 10.        , 192.9789515 ],\n",
       "       [ 11.        , 158.13596717],\n",
       "       [ 12.        , 173.66449921],\n",
       "       [ 13.        , 158.88094424],\n",
       "       [ 14.        , 146.82333677],\n",
       "       [ 15.        , 149.30869477],\n",
       "       [ 16.        , 151.35364002],\n",
       "       [ 17.        , 179.11480955],\n",
       "       [ 18.        , 188.53891808],\n",
       "       [ 19.        , 195.23442863],\n",
       "       [ 20.        , 157.38833412]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_high = np.random.uniform(140,200,size=20)\n",
    "female_id=np.arange(1,21)\n",
    "female_high=female_high.reshape(20,-1)\n",
    "female_id=female_id.reshape(20,1)\n",
    "female=np.concatenate((female_id,female_high),axis=1)\n",
    "female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.        , 166.1626436 ],\n",
       "       [  2.        , 172.02685916],\n",
       "       [  3.        , 188.00236405],\n",
       "       [  4.        , 171.3714187 ],\n",
       "       [  5.        , 171.01564533],\n",
       "       [  6.        , 186.95387017],\n",
       "       [  7.        , 159.11322664],\n",
       "       [  8.        , 167.00945137],\n",
       "       [  9.        , 171.53605157],\n",
       "       [ 10.        , 199.75500141],\n",
       "       [ 11.        , 167.26511138],\n",
       "       [ 12.        , 140.07715282],\n",
       "       [ 13.        , 148.22223803],\n",
       "       [ 14.        , 164.46384483],\n",
       "       [ 15.        , 188.09165811],\n",
       "       [ 16.        , 155.74431424],\n",
       "       [ 17.        , 159.50046284],\n",
       "       [ 18.        , 183.62316487],\n",
       "       [ 19.        , 161.62502985],\n",
       "       [ 20.        , 146.71965895],\n",
       "       [  1.        , 146.4799221 ],\n",
       "       [  2.        , 196.46630789],\n",
       "       [  3.        , 179.16361409],\n",
       "       [  4.        , 191.11168056],\n",
       "       [  5.        , 169.75145576],\n",
       "       [  6.        , 142.19080864],\n",
       "       [  7.        , 171.1102571 ],\n",
       "       [  8.        , 176.4684961 ],\n",
       "       [  9.        , 156.94563434],\n",
       "       [ 10.        , 192.9789515 ],\n",
       "       [ 11.        , 158.13596717],\n",
       "       [ 12.        , 173.66449921],\n",
       "       [ 13.        , 158.88094424],\n",
       "       [ 14.        , 146.82333677],\n",
       "       [ 15.        , 149.30869477],\n",
       "       [ 16.        , 151.35364002],\n",
       "       [ 17.        , 179.11480955],\n",
       "       [ 18.        , 188.53891808],\n",
       "       [ 19.        , 195.23442863],\n",
       "       [ 20.        , 157.38833412]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=np.concatenate((male,female),axis=0)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=np.concatenate([np.ones(20),np.zeros(20)])\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.        , 166.1626436 ],\n",
       "       [  2.        , 172.02685916],\n",
       "       [  3.        , 188.00236405],\n",
       "       [  4.        , 171.3714187 ],\n",
       "       [  5.        , 171.01564533],\n",
       "       [  6.        , 186.95387017],\n",
       "       [  7.        , 159.11322664],\n",
       "       [  8.        , 167.00945137],\n",
       "       [  9.        , 171.53605157],\n",
       "       [ 10.        , 199.75500141],\n",
       "       [ 11.        , 167.26511138],\n",
       "       [ 12.        , 140.07715282],\n",
       "       [ 13.        , 148.22223803],\n",
       "       [ 14.        , 164.46384483],\n",
       "       [ 15.        , 188.09165811],\n",
       "       [ 16.        , 155.74431424],\n",
       "       [ 17.        , 159.50046284],\n",
       "       [ 18.        , 183.62316487],\n",
       "       [ 19.        , 161.62502985],\n",
       "       [ 20.        , 146.71965895]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[y == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x[y ==1,0],x[y==1,1],color='r')\n",
    "plt.scatter(x[y ==0,0],x[y==0,1],color='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=np.arange(10)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.17022005e-01, 7.20324493e-01, 1.14374817e-04, 3.02332573e-01,\n",
       "       1.46755891e-01, 9.23385948e-02, 1.86260211e-01, 3.45560727e-01,\n",
       "       3.96767474e-01, 5.38816734e-01])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "y=np.random.random(size=10)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,color='y',linestyle='--',marker=\"h\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3deXxU5fX48c8hLBIUQUBFtiCiQkbb2ri0Wq0boqJoRUVxtwX0S1uLrVpxrVJbtf3WPSKuFaVuVVpx+VZtXepCsC4goilrBCUqihIkBM7vj5P8GMKETMjMPHPvnPfrxWsydy5zj8GcPPPc85xHVBXnnHPR1yZ0AM455zLDE7pzzsWEJ3TnnIsJT+jOORcTntCdcy4m2oa6cPfu3bWkpCTU5Z1zLpJmzpz5qar2SPVasIReUlJCRUVFqMs751wkicjCpl7zKRfnnIsJT+jOORcTntCdcy4mPKE751xMeEJ3zrmY8IS+GaZMgZISaNPGHqdMCR2Rc84FLFuMqilTYPRoqKmx5wsX2nOAUaPCxeWccz5Cb6EJE9Yn8wY1NXbcOedC8oTeQosWtey4c87liif0Furbt2XHnXMuVzyht9DEidCx44bHiovtuHPOhZRWQheRoSIyV0QqReSiFK//SkTeqv8zS0TWisg2mQ83vFGj4I471o/Id9gBJk3yG6LOufCarXIRkSLgFuBQoAqYISLTVPW9hnNU9TrguvrzjwJ+oaqfZyfksF5/Hbp1g/nzrWzROefyRTopaS+gUlXnqWotMBUYvonzTwIezERw+ejKK+Hss2HtWitZfPTR0BE555xJJ6H3AhYnPa+qP7YRESkGhgIp05yIjBaRChGpqK6ubmmswc2fD08/DT/+MbRrBw89BCNGwOex/CzinIuadBK6pDimTZx7FPBKU9MtqjpJVctUtaxHj5T92fPaHXeAiCV0gETCHmfPDheTc841SCehVwF9kp73BpY0ce5IYjrdUlsLd94Jw4ZBn/rvhid051w+SSehzwAGikh/EWmPJe1pjU8Ska2BA4AnMhtifqistGmWsWPXH+vdGzp3hlmzwsXlnHMNmq1yUdU6ERkHPAMUAXep6mwRGVv/enn9qccCz6rqyqxFG9DgwbBgwYaVLSJQWuoJ3TmXH0S1qenw7CorK9Oo7Cn65Ze2eKhdu41fmzULunSx0bpzzmWbiMxU1bJUr3kldRquvBJ22glWr974tUTCk7lzLj94Qm/GqlVwzz2wzz7QocPGr3/+OVx/vd8Ydc6F5/3Qm/Hww7B8+YY3Q5PV1cGvfmXTMaWluY3NOeeS+Qi9GeXlsPPO8MMfpn59222hRw+/MeqcC88T+ibMmgWvvmqjc0m1vKqeV7o45/KBJ/RNGDwY/vEPOP30TZ+XSNgceqCCIeecAzyhb1KbNnDwwbBNM42AEwnbhm7p0tzE5ZxzqXhCb8KUKTB+PHzzTfPnnnoqrFxpvdGdcy4Ur3JJQRX+8AdrkZuqVLGx4uLsx+Scc83xEXoKM2bAf/7T/M3QZFdfDdddl924nHNuUzyhp1BeDp06tWxbuZdfhgdj2WfSORcVntAbWb4cpk61ZN65c/p/L5GAOXNsmsY550LwhN5ITQ2ccELTK0ObkkjYDdR587ITl3PONcdvijbSq5f1bmmphmX/s2bBwIEZDck559LiI/Qkc+bAm29u3t8dPBh69oSvv85sTM45ly4foSe56iqYPt0WCHXs2LK/26kTLGlqYz7nnMsBH6HXW7YMHnnElvm3NJk751w+8IRe7557YM0aGDNm89/jgQdg991tQ2nnnMu1tBK6iAwVkbkiUikiFzVxzg9F5C0RmS0i/8psmNm1bh3cfjvsv7/NhbfGu+/CBx9kJi7nnGuJZhO6iBQBtwCHA4OBk0RkcKNzugC3AkerailwfBZizZoPPoDq6paXKjaWSNijt9J1zoWQzk3RvYBKVZ0HICJTgeHAe0nnnAw8pqqLAFR1WaYDzaZdd7Ubmu3bt+59dtkFioo8oTvnwkhnyqUXsDjpeVX9sWQ7A11F5J8iMlNETkv1RiIyWkQqRKSiurp68yLOsNpaa8a15ZatT+gdOtjuRr6/qHMuhHQSeqr2VI23cmgLfBc4EjgMuFREdt7oL6lOUtUyVS3r0aNHi4PNht/9zm5krlqVmfc75hgb8TvnXK6lM+VSBfRJet4baFxxXQV8qqorgZUi8iLwLSCvbw/W1cEdd9iN0EyVKv72t5l5H+eca6l0RugzgIEi0l9E2gMjgWmNznkC+IGItBWRYmBvYE5mQ8286dOhqqr1N0MbU7VfFs45l0vNJnRVrQPGAc9gSfohVZ0tImNFZGz9OXOAp4F3gDeAyaqa97cGy8ttl6FhwzL3nosWQdeutuORc87lUlpL/1V1OjC90bHyRs+vAyKzxcP8+fD003DppdCuXebed4cdrOui3xh1zuVawfZy2WEHG0Xvv39m37dtWxg0yEsXnXO5V7AJvUMHOOmk7Lx3aSm8+GJ23ts555pSkL1cnnrKyhW/+SY7759IwOLF8OWX2Xl/55xLpSAT+nXXWe+WTM6dJzv4YLj4Yq90cc7lVsFNubz/PrzwgtWLFxVl5xp77ml/nHMulwpuhD5pkt24POus7F7nq6+shNE553KloEboq1ZZ3/Mf/Qi22y671zrkEOsP89xz2b2Oc841KKgR+qefwj77wDnnZP9aiYSXLjrncqugEnqfPrbc/4c/zP61Egnb1i5Pmko65/LAlClQUgJt2thjpleUF0xCr6rK7Zx2aak9+opR5xxY8h49GhYutH5PCxfa80wm9YJJ6L/9ra3gXLkyN9fz3Yucc8kmTICamg2P1dTY8UwpiJuiX30Ff/4zjBgBnTrl5po9e1rzr1xM7zjn8l9TMwSZnDkoiIT+4IPw9deZb5O7KSIwZkzuruecy29bbw1ffLHx8b59M3eN2E+5qNpIeffdrcIll5Ysgb/+1WJwzhWuhx6yZN54MWNxMUycmLnrxD6h//e/8O67NjqXVJvpZdHjj1vN+5LG+zs55wrGrFlw2mmw334weTL062e5qF8/W+g4alTmrhX7KZeddrJGWVtumftrJ98Y7dV4W23nXEEYNAguu8ymYLt1gzPOyN61Yj1Cb5jq2H77MAm9oXTRK12cKzyffmrl0kVF1qyvW7fsXzPWCf2mm+Cgg+yGaAjdutkvE0/ozhWWVavg6KOtyq22NnfXje2US8PN0K22CjM6b+AtAJwrLOvWwemnw2uv2c3Q9u1zd+20RugiMlRE5opIpYhclOL1H4rIlyLyVv2fyzIfasu89BLMmZPbUsVU/vd/4S9/CRuDcy53fv1rePhh23dhxIjcXrvZEbqIFAG3AIcCVcAMEZmmqu81OvUlVR2WhRg3S3m51X2eeGLYOBpujDrn4u/BB+Haa+Hcc2H8+NxfP50R+l5AparOU9VaYCowPLthtc6yZfDII/axp7g4bCwrVsCNN8Jbb4WNwzmXfUccAVdcATfckPsyaUgvofcCFic9r6o/1tj3RORtEXlKREpTvZGIjBaRChGpqM5iG8KOHe3jzrnnZu0SLfLzn1uXR+dcPH34ofVl2XpruPxy20QnhHQSeqrfM43XPr4J9FPVbwE3AY+neiNVnaSqZapa1qNHj5ZF2gJbbWVJdJddsnaJtHXubAsI/Maoc/FUVWXVLKefHjqS9BJ6FdAn6XlvYIO1j6q6QlW/rv96OtBORLpnLMoWeO01uOsuWL06xNVTSyS8ja5zcbRiBRx5pDUAvCx4KUh6CX0GMFBE+otIe2AkMC35BBHZXsRmjERkr/r3/SzTwabj2mvhwgtDXLlpiYRtTr1mTehInHOZsmYNHH88vPcePPoo7LZb6IjSqHJR1ToRGQc8AxQBd6nqbBEZW/96OTACOEdE6oBVwEjV3Lek+ugjmDYNzj8fOnTI9dWbVlpq//gLFsDAgaGjcc5lwkUXwbPPWn+WQw8NHY2RAHkXgLKyMq2oqMjoe/7mN3ZDorISBgzI6Fu3Sk2NLXTKVS9251z2ffihFTv8/Oe5va6IzFTVslSvxWbpf10d3HEHDBmSX8kcrHTSk7lz8fDuuzZAGzgw98m8ObFJ6EuXwrbbhl8Z2pQ//tG2wXPORddLL0FZGVx/fehIUotNL5c+fSDDMzgZ9fLLdvPk4otDR+Kc2xxz58Ixx0D//nD22aGjSS0WI/TPP7fdQETCrM5KRyJhc27ffBM6EudcS1VX2yrQoiKbN99mm9ARpRaLhH799bYv34oVoSNpWiJhXdjefz90JM65llCF446zncf+9jfYccfQETUt8lMutbVw551w4IG2KjNfNTTpmj0bvv3tsLE459InApdcYtVqe+8dOppNi3xCf/xxa8aVrzdDGwwcaJtdfPVV6Eicc+maNcsGY0OGhI4kPZGfcikvh5KS/P+Gt2tnlTj5/ovHOWduvRV23x2efz50JOmLdEJfuBBeeAFGj7abFc45lwlPPgk//SkMGwYHHBA6mvRFOqH362dz0qNHh44kPU88YfPnPu3iXP56803bGOc737ENK6I0WIx0QgcYPDg3u2lnyttvWz26cy7/LF9uo/Ju3ayiJWorvCOb0B980H6Lfvll6EjSl1zp4pzLP1272uK/J5+Enj1DR9Nyka1yuekm+Oyz/C5VbKx/f9tNyTe7cC6/rFkD8+fDzjvDuHGho9l8kRqhT5liFS1t2sCrr8Kee+bvytBU2rSxKSJP6M7lD1UYM8byyccfh46mdSKT0KdMsZufCxfaPwDAY4/Z8Sg58sj82BrPOWcmToS774bzzrO1IlEWmX7oJSWWzBvr1882jnDOuZa6/3449VT7c++90fjEH4t+6IsWtex4PlOFtWtDR+FcYZs5E846yzZ4njw5Gsm8OZFJ6H37tux4vvr0U+vbPmlS6EicK2y77Wb7Dz/2GLRvHzqazEgroYvIUBGZKyKVInLRJs7bU0TWisiIzIVoJk60nX+SFRfb8Sjp1s0aivmNUefCWLbMBlbt28NVV1mpYlw0m9BFpAi4BTgcGAycJCKDmzjv99hm0hk3apSNavv1s49G/frZ81GjsnG17BGxenRP6M7lTnKFXJ8+sMce8Zz2TGeEvhdQqarzVLUWmAoMT3HeT4FHgWUZjG8Do0bZDdB16+wxasm8QSJhi4sC3Y92rqA0rpCrrbVR+tSpoSPLvHQSei9gcdLzqvpj/5+I9AKOBco39UYiMlpEKkSkorq6uqWxxkZpqS2K+uST0JE4F38TJlgv82SrV9vxuEknoae699t4bPkn4EJV3eSHGFWdpKplqlrWo0ePdGOMnf33h1/+0kfozmVTXZ09xqlCrjnpLP2vAvokPe8NLGl0ThkwVazupztwhIjUqerjGYkyZr79bd+1yLlsWbIE7rjD7rFNm2aVcKnWsEStQi4d6YzQZwADRaS/iLQHRgLTkk9Q1f6qWqKqJcAjwLmezDdt1SpYvLj585xzzVOFf/4TTjjBCiauuMI2pxCJT4VcOpodoatqnYiMw6pXioC7VHW2iIytf32T8+YutWHDbF7v1VdDR+JcdKla0q6pgeHDoW1bW8I/diwMGGDn7LGHPU6YYNMsfftaMo9qUcWmRGbpf9z87GfWP2LFinisUHMul2bNsi3i3nzTBkUi8PrrNirv2DF0dNkVi6X/cZNIwNdfx/PGjHPZUFsLf/mLbQm3225w110waBCsXGmv7713/JN5czyhB9Kw2YUvMHIuPY8/DiNHQlUVXHcdfPSRfcrdcsvQkeUPT+iBDK5fa+sJ3bmNqcJzz8Fxx1nyBjjmGJg+HT780Mp+o7T1ZK54Qg+kSxcoL4cjjggdiXP544sv4MYbbSrlkEPgX/9av0lz+/Zw+OG2fN+lFtkt6OJgzJjQETiXX84806ZW9t4b7rsPjj8ettgidFTR4b/rAqqutp3FG1a0OVdIVq+GBx6wldMNm9RcfjlUVMBrr9mmE57MW8YTekBPPglHHw3z5oWOxLnsSO5yWFJizxctgosvtq6Ho0bB0qV2oxNsBfV3vxsy4mjzKZeAkitddt45bCzOZVpDl8OGxlgLF8JPfgJr1ljH1KOOgnPPtblynxfPDE/oAQ0aZAsiZs2CH/0odDTOZVaqLoerVkH37jat0q9fmLjizH8vBtSpE+y4o/VGdy5umlo099lnnsyzxRN6YKWlXovu4ql379TH49jlMF/4lEtgV19tDYWcixNV2GGHjTuKxrXLYb7wEXpgu+1mc+nOxcm8eTBnDpx4YvT3AY4SHxsGVlNjCyjKyuyPc3EwYIDdG+rVy7uJ5pKP0AMrKoJx42x1nHNR98kncNttNuXSu7cn81zzhB5Yhw5Wg+43Rl3U1dXBSSfB+PHrV3663PIplzyQSFijfuei7NJL4YUX4N57oX//0NEUJh+h54FEwm4iNV6E4VxUPPEE/O53tjL0tNNCR1O40kroIjJUROaKSKWIXJTi9eEi8o6IvCUiFSKyX+ZDja9EwuYcP/ggdCSbJ1W/Dlc4VqywLonf/S7ccEPoaApbs1MuIlIE3AIcClQBM0Rkmqq+l3Tac8A0VVUR2R14CNg1GwHH0dChsHy59UiPmlT9OkaPtq+9PK0wdO4MU6favSDvjhhWOiP0vYBKVZ2nqrXAVGB48gmq+rWu3226ExBm5+mIKi6OZjKH1P06amrsuIs31fVtK4YMsU9nLqx0EnovIHm9V1X9sQ2IyLEi8j7wJHBWZsIrHOXlcNVVoaNouab6dfjm1/E3ebItjHvxxdCRuAbpJPRUlaQbjcBV9a+quitwDJAyNYnI6Po59orq6uqWRRpzr7xiq+iipqm+HN6vI94qKmz9xKGHwr77ho7GNUgnoVcBfZKe9waWNHWyqr4IDBCR7ilem6SqZapa1qNHjxYHG2eJhDX5/+KL0JG0zOWXb3xMJPVxFw+ffw4jRsB229k9lIY9P1146ST0GcBAEekvIu2BkcC05BNEZCcRWxMmInsA7YHPMh1snJWW2uN77236vHy13XaWyHv0sLnVf/3LHl28rFsHp5wCS5bAI49Yb3OXP5pN6KpaB4wDngHmAA+p6mwRGSsiY+tPOw6YJSJvYRUxJybdJHVpSN69KEpuuw0GD7ZtxNatg2XLbHR+771w662ho3OZJmI7DN10E+y1V+hoXGNprRRV1enA9EbHypO+/j3w+8yGVlj69oWePa2mNyqWL4eVK2Hs2A17dlx2Gbz9NtTWhovNZV5dnbV6Hj8+dCSuKb70P0+0aQMffRStZkZdu9onirVrNzzepg089li0/lvcpi1aBAcfbNVYBx8cOhrXFF/6n0eilABramx0LpJ6g46G/5ann4Yjj/TRepStXg3HH2+dFPv0af58F44n9Dzy7LM2LxmFis6777YdaaqqNn3eihUwfTqcd15u4nKZN348vPEG3HOPrQZ1+csTeh4RgRkz8v/GqKrdDB04sOl9IxuccAL86ld2/t135yY+lzn33283t3/5S/jRj0JH45rjCT2PRKXS5ZVXbMn3Oeekd/5vf2vzruecYwtSXHS88grsvz9cc03oSFw6PKHnke23h222yf+EXl5uDZlGjkzv/LZtrXnT9tvDX/6S3dhcZt16Kzz1lG9kHhWe0POIiC0wamh4lI+WL4eHH7ae1506pf/3une3edhrr81ebC4zVG2abM4c+3+yuDh0RC5d/ns3zxx6qG12ka+6doVXX7VPEi217bb2+P77Nur7xS8yG5vLjD/+Ea6/3m56DxoUOhrXEp7Q88yll4aOoHl77NG6v3/HHZY0dtgBTjwxMzG5zHjxRbjwQrsB6pVJ0eNTLnkqHxsnPP88nHFG68sqr7nGOvSddRa8+25GQnMZ8PHH9gt2xx2tIilK6yKc8YSeZ1auhF69bASbb26+GZ580m6Itkb79jYPv/XWcOyxNi/vwps40dYNPPZY6/+NXRie0PNMp07W5CrfKl0++gimTbNRdYcOrX+/nj2tW9+iRVbW6MK7/np47rn15bMuenwOPQ8lEvmX0CdPtp4tY8Zk7j2//3145hn43vcy956u5V5+2aqrunaFffYJHY1rDR+h56FEwkoX160LHYmpq7MbmYcdZvOrmXTggbax8BdfwL//ndn3ds374AM44gjrmOmizxN6HiothVWrYP780JGYmhpbwp/NqoexY+Hww2Hu3Oxdw21o5Uo47ji7p3HddaGjcZngCT0Pfe978NOf5s/qvM6d7Sbt0KHZu8a111piOfZY+Oqr7F3HGVX7JTp7NjzwgO8BGxee0PNQaSnceCP06xc6Eli82G6UZbuMsm9fawswdy6ceWZ+lm3GyV13WeOtK6+EIUNCR+MyxRN6nqqttcqS0G6+2ebOly7N/rUOOshG6o8+ar/QXPYccQRMmGB/XHzkyYd619iIEbBgAbzzTrgYVq+2kdzRR9uqzlwYP97uHxx/fG6uV2hWrLDS2J494eqrQ0fjMi2tEbqIDBWRuSJSKSIXpXh9lIi8U//n3yLyrcyHWlgGD7aeJ2vWhIvh0Ufh009zWwEhApdcYr9A1q6NxmYfUbF2rf2iHD7cp7TiqtmELiJFwC3A4cBg4CQRGdzotPnAAaq6O3AVMCnTgRaaRMKS+YcfhouhvBwGDLBd3kM4+WSb362pCXP9uPnNb2xXrGOO8WX9cZXOCH0voFJV56lqLTAVGJ58gqr+W1UbFnC/BjSzj41rTsNqvVCtdJcvh8pKW0jUJtCdltNOg7ffthh8RNk606dbQj/zTDj77NDRuGxJ50e1F7A46XlV/bGmnA08leoFERktIhUiUlHtn6U3adddLZGGWjHatSssXAjjxoW5Ptjm0ldcYdUYN98cLo6omz8fTjkFvvUtuOUWH53HWToJPdU/f8rxkogciCX0C1O9rqqTVLVMVct69OiRfpQFaIst7IfvqKNyf+3aWlsd2q4ddOyY++snu+QSuyk7fjy89FLYWKJkyhQoKbFBwb77wpZb2j2R0P+eLrvSSehVQJ+k572BJY1PEpHdgcnAcFX9LDPhFbaxY6GsLPfXve8+Swb5UDbZpo3Fc8ABmWkKVgimTIHRo+0TlqqVnH72Gbz2WujIXLaJNjM5KSJtgQ+Ag4GPgBnAyao6O+mcvsDzwGmqmlZHjrKyMq3wHYM3aflymDEj98msrMxKFt95Jz8/nq9bF25ePwpKSiyZN9avn5XCumgTkZmqmnKo1+yPharWAeOAZ4A5wEOqOltExopIQ0HbZUA34FYReUtEPFNnwP/9ny3qmTMnd9esqICZM+3TQb4lc1U4/3w499zQkeS3RYtadtzFR1oLi1R1OjC90bHypK9/DPw4s6G5hkqXWbPg29/OzTXLy23hyamn5uZ6LSFi8/q33w577unVGqn8+9/26WXt2o1f834t8ecfXPPYwIGWwHJV6fLFF9ao6eST83fHmokTbSPtc8+FN94IHU1++eorGDYMunWzm+rJiovte+fizRN6HmvXzsoXc1WL3rmzbT92/vm5ud7mKCqCBx+0pevHHQfLloWOKLyVK206aqut4IknrMHZ5Mk2Zy5ij5MmwahRoSN12eYJPc/lcveiNm2sRe4uu+TmepurWzf4619tBel//hM6mrDeesvqy2+7zZ7/4AfQpYsl7wUL7AbyggWezAuFJ/Q8d/HF8Pjj2b/Oq6/CBRdEZ8Pm73zHEtVhh4WOJAxV20Vqn33gm28sqTvnCT3PJRK5+WG96Sb7WB6lWu+ttrLHe++FqVPDxpJLK1fCGWdYrfn++9unlH33DR2Vywee0PNcba21sH399exdY9kyeOQROP10u3kWJWvXwp13wllnhW01nEuvv26Lh664Ap56CnzRtWvgCT3PtW1r29FlcwR6993W2TGKGwUXFcFDD1nvmUMOgT597F5ASYklvThp2G/1oINsc+fLL7f/fucaeELPc23aWG/0bN0YXbfO6roPOAAGDcrONbJt++3hJz+x3ulVVTa/vHChTUnEIamvXm1lmqWltnIYYMcdw8bk8pMn9AhIJLJXurhixfpNqaPsnns2PlZTE/0t1hYsgP32syqW887L3QIzF02+BV0ElJZawvrsMyvZy6QuXeIxim1qWfvChfD557DNNrmNJxP+/nfrCb9unZVpHnNM6IhcvvMRegQ0tADIdE+XTz6Bd9/N7HuGsqll7X362JRFwxx0VMyaZfcCZs70ZO7S4wk9Ag44wCpR9tsvs+97661WErlko2bI0TNx4sYVOsXFcM01MHKkVQrtuqv1qslnS5fCyy/b1xdcYL1ZBgwIG5OLDp9yiYCOHTO/McGaNbYwZehQ25A56hpWQk6YYNMvfftakm84fs01Ng/dsD/qjBk2Aj7ppI37noTy/PPr4/nwQ2jfPn9ic9HgI/SIuO8+qzvOlL/9zUaD55yTufcMbVPL3bfd1sr8dtrJnt9/v9Wu9+sHV14ZtifMunVw9dXWdKxbN6stb98+XDwuujyhR8Qrr9hqzkxtllxebnPLRxyRmfeLmj/9Cf7xD2vDe8UVNqK/MOXGidm1apXtnXrppTY6f+MNK1N1bnN4Qo+IRMKqNT7+uPXv9fnnth3ZT35SuAtTRODgg62S5P33bbTeUEG0di08+6yNnLNtiy2sjv622+DPf7a9P53bXD6HHhHJm1307Nm699pmm/ULcJx1l7z11vXP//Y3OPZYO37eeVY6mMmWCKpw880wZIhd4+67M/ferrD5CD0iSkvtsbUrRtets4TSuTNsvXXr44qjI4+02vyttrJ7DH36WNfLmprWv/eXX8Lxx8PPfmY3pZ3LpLQSuogMFZG5IlIpIheleH1XEXlVRFaLyC8zH6bbdlub5/3yy9a9zwMPWOvZpUszE1cctWtnuza98Qa89JKVjT722PqKk829gfr227YB9+OPw7XXwnXXZS5m5yCNKRcRKQJuAQ4FqoAZIjJNVd9LOu1z4GeAL3/IogULWr9xc3m5jTS32y4jIcWaiNX+77ef3bxs08a+d4MG2RTYL34BRx2V3n2Il1+2KpZttoEXXrCNKJzLtHRG6HsBlao6T1VrganA8OQTVHWZqs4A1mQhRlevtZoqmG0AAAevSURBVMn83XetWmbMGEtOLn3J6wAmTLCWAsceCzvvDDfcYPt5bkpZGfz4x9a73JO5y5Z0fqx7AYuTnlfVH3M59vLLlgya6lvSnNtvtw0szjgjo2EVlOJiGD8eKivh4YetQuW889b3Yle1+feSEvulWVxs+3tusYWVnW67bdDwXcylk9BTjQs3qz5CREaLSIWIVFRXV2/OWxQ0EUvqm9N/5euvbXHSCSdkvsFXIWrbFkaMsE88b78N3/++HT/sMNsoZOFCS+6rVlknyzg0QHP5L52EXgX0SXreG9is7h+qOklVy1S1rIdvs9Jiral0adfOSuXGj89sTA523339dNgbb1gde7Jvvol+G18XDenUoc8ABopIf+AjYCRwclajcil16QK9e29eQu/QweqpXXatWJH6+OZOkznXEs2O0FW1DhgHPAPMAR5S1dkiMlZExgKIyPYiUgWMBy4RkSoR6ZzNwAtVItHyhP7WW/CHP9i0i8uuptr4bqq9r3OZklatg6pOV9WdVXWAqk6sP1auquX1X3+sqr1VtbOqdqn/uomximuNAw9c32AqXTfeaP1KfGVo9jXVxnfixDDxuMLiS/8j5oILWnb+8uW2wfRpp9nKR5ddzbXxdS6bPKFHlGp6den33WeVFmPHZj8mZ0aN8gTuwvDlJRGzZo1NuVx9dfPnqtrK0H328c2FnSsEPkKPmHbt7DGdWvQvvrDGUqeckt2YnHP5wRN6BKVb6dK1q/X1ds4VBp9yiaBEwvacXL266XOWL4ePPspdTM658DyhR1AiAXV18MEHTZ9TXm79RLxNrnOFwxN6BJWVWdVKUzvCr10LkybB/vu3fncj51x0+Bx6BO20k+1B2ZRnnrHe6ddem7OQnHN5wEfoEbV2bdMbRpeX2wYWw4enft05F0+e0CPq1FNh3303Pv7ZZzZCP/tsaN8+93E558LxKZeI2nVXW9K/ciV06rT+eLdu8N//WndF51xh8RF6RCUSthJ0zpyNX+vdG7zdvHOFxxN6RCUS9pi8wOjRR2HIEPjkkzAxOefC8imXiBowwKZVZs9ef+y222zBUffu4eJyzoXjCT2iiopst/nddrPnH3wAzz1nTbuKisLG5pwLwxN6hI0Zs/7r22+3jYvPPjtcPM65sHwOPcK++gr++U8rVbznHjj2WNh++9BROedC8RF6hL34IgwbZh0Vf/3r1HXpzrnCkdYIXUSGishcEakUkYtSvC4icmP96++IyB6ZD9U1Vllpj0OGwM03w7x5YeNxzoXVbEIXkSLgFuBwYDBwkogMbnTa4cDA+j+jgU10GnGZMGUKXHzx+ucLF8Lo0XbcOVeY0hmh7wVUquo8Va0FpgKNu4QMB+5T8xrQRUS8z18WTZgANTUbHqupsePOucKUTkLvBSxOel5Vf6yl5yAio0WkQkQqqqurWxqrS7JoUcuOO+fiL52Enmpved2Mc1DVSapapqplPXxteqv07duy4865+EsnoVcBfZKe9waWbMY5LoMmToTi4g2PFRfbcedcYUonoc8ABopIfxFpD4wEpjU6ZxpwWn21yz7Al6rqm59l0ahRtitRv34gYo+TJtlx51xharYOXVXrRGQc8AxQBNylqrNFZGz96+XAdOAIoBKoAc7MXsiuwahRnsCdc+ultbBIVadjSTv5WHnS1wr8T2ZDc8451xK+9N8552LCE7pzzsWEJ3TnnIsJT+jOORcTYvczA1xYpBpYuJl/vTvwaQbDiTr/fmzIvx/r+fdiQ3H4fvRT1ZQrM4Ml9NYQkQpVLQsdR77w78eG/Puxnn8vNhT374dPuTjnXEx4QnfOuZiIakKfFDqAPOPfjw3592M9/15sKNbfj0jOoTvnnNtYVEfozjnnGvGE7pxzMRG5hN7chtWFRET6iMgLIjJHRGaLyM9DxxSaiBSJyH9E5O+hYwlNRLqIyCMi8n79/yPfCx1TKCLyi/qfkVki8qCIbBE6pmyIVEJPc8PqQlIHnK+qg4B9gP8p8O8HwM+BOaGDyBM3AE+r6q7AtyjQ74uI9AJ+BpSpagJrAz4ybFTZEamETnobVhcMVV2qqm/Wf/0V9gO70V6uhUJEegNHApNDxxKaiHQG9gfuBFDVWlX9ImxUQbUFOopIW6CYmO6oFrWEntZm1IVIREqA7wCvh40kqD8BFwDrQgeSB3YEqoG766egJotIp9BBhaCqHwHXA4uApdiOas+GjSo7opbQ09qMutCIyJbAo8B5qroidDwhiMgwYJmqzgwdS55oC+wB3Kaq3wFWAgV5z0lEumKf5PsDOwCdROSUsFFlR9QSum9G3YiItMOS+RRVfSx0PAHtCxwtIguwqbiDROT+sCEFVQVUqWrDJ7ZHsARfiA4B5qtqtaquAR4Dvh84pqyIWkJPZ8PqgiEigs2RzlHVP4aOJyRV/bWq9lbVEuz/i+dVNZajsHSo6sfAYhHZpf7QwcB7AUMKaRGwj4gU1//MHExMbxCntadovmhqw+rAYYW0L3Aq8K6IvFV/7OL6PWCd+ykwpX7wM48C3bxdVV8XkUeAN7HKsP8Q0xYAvvTfOediImpTLs4555rgCd0552LCE7pzzsWEJ3TnnIsJT+jOORcTntCdcy4mPKE751xM/D8g3dJdFjYjDAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,'b--o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.1010101 ,  0.2020202 ,  0.3030303 ,  0.4040404 ,\n",
       "        0.50505051,  0.60606061,  0.70707071,  0.80808081,  0.90909091,\n",
       "        1.01010101,  1.11111111,  1.21212121,  1.31313131,  1.41414141,\n",
       "        1.51515152,  1.61616162,  1.71717172,  1.81818182,  1.91919192,\n",
       "        2.02020202,  2.12121212,  2.22222222,  2.32323232,  2.42424242,\n",
       "        2.52525253,  2.62626263,  2.72727273,  2.82828283,  2.92929293,\n",
       "        3.03030303,  3.13131313,  3.23232323,  3.33333333,  3.43434343,\n",
       "        3.53535354,  3.63636364,  3.73737374,  3.83838384,  3.93939394,\n",
       "        4.04040404,  4.14141414,  4.24242424,  4.34343434,  4.44444444,\n",
       "        4.54545455,  4.64646465,  4.74747475,  4.84848485,  4.94949495,\n",
       "        5.05050505,  5.15151515,  5.25252525,  5.35353535,  5.45454545,\n",
       "        5.55555556,  5.65656566,  5.75757576,  5.85858586,  5.95959596,\n",
       "        6.06060606,  6.16161616,  6.26262626,  6.36363636,  6.46464646,\n",
       "        6.56565657,  6.66666667,  6.76767677,  6.86868687,  6.96969697,\n",
       "        7.07070707,  7.17171717,  7.27272727,  7.37373737,  7.47474747,\n",
       "        7.57575758,  7.67676768,  7.77777778,  7.87878788,  7.97979798,\n",
       "        8.08080808,  8.18181818,  8.28282828,  8.38383838,  8.48484848,\n",
       "        8.58585859,  8.68686869,  8.78787879,  8.88888889,  8.98989899,\n",
       "        9.09090909,  9.19191919,  9.29292929,  9.39393939,  9.49494949,\n",
       "        9.5959596 ,  9.6969697 ,  9.7979798 ,  9.8989899 , 10.        ])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=np.linspace(0,10,100)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.10083842,  0.20064886,  0.2984138 ,  0.39313661,\n",
       "        0.48385164,  0.56963411,  0.64960951,  0.72296256,  0.78894546,\n",
       "        0.84688556,  0.8961922 ,  0.93636273,  0.96698762,  0.98775469,\n",
       "        0.99845223,  0.99897117,  0.98930624,  0.96955595,  0.93992165,\n",
       "        0.90070545,  0.85230712,  0.79522006,  0.73002623,  0.65739025,\n",
       "        0.57805259,  0.49282204,  0.40256749,  0.30820902,  0.21070855,\n",
       "        0.11106004,  0.01027934, -0.09060615, -0.19056796, -0.28858706,\n",
       "       -0.38366419, -0.47483011, -0.56115544, -0.64176014, -0.7158225 ,\n",
       "       -0.7825875 , -0.84137452, -0.89158426, -0.93270486, -0.96431712,\n",
       "       -0.98609877, -0.99782778, -0.99938456, -0.99075324, -0.97202182,\n",
       "       -0.94338126, -0.90512352, -0.85763861, -0.80141062, -0.73701276,\n",
       "       -0.66510151, -0.58640998, -0.50174037, -0.41195583, -0.31797166,\n",
       "       -0.22074597, -0.12126992, -0.0205576 ,  0.0803643 ,  0.18046693,\n",
       "        0.27872982,  0.37415123,  0.46575841,  0.55261747,  0.63384295,\n",
       "        0.7086068 ,  0.77614685,  0.83577457,  0.8868821 ,  0.92894843,\n",
       "        0.96154471,  0.98433866,  0.99709789,  0.99969234,  0.99209556,\n",
       "        0.97438499,  0.94674118,  0.90944594,  0.86287948,  0.8075165 ,\n",
       "        0.74392141,  0.6727425 ,  0.59470541,  0.51060568,  0.42130064,\n",
       "        0.32770071,  0.23076008,  0.13146699,  0.03083368, -0.07011396,\n",
       "       -0.17034683, -0.26884313, -0.36459873, -0.45663749, -0.54402111])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=np.sin(x)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  0.99490282,  0.97966323,  0.95443659,  0.91948007,\n",
       "        0.87515004,  0.8218984 ,  0.76026803,  0.69088721,  0.61446323,\n",
       "        0.53177518,  0.44366602,  0.35103397,  0.25482335,  0.15601496,\n",
       "        0.0556161 , -0.04534973, -0.14585325, -0.24486989, -0.34139023,\n",
       "       -0.43443032, -0.52304166, -0.60632092, -0.68341913, -0.75355031,\n",
       "       -0.81599952, -0.87013012, -0.91539031, -0.95131866, -0.97754893,\n",
       "       -0.9938137 , -0.99994717, -0.9958868 , -0.981674  , -0.95745366,\n",
       "       -0.92347268, -0.88007748, -0.82771044, -0.76690542, -0.69828229,\n",
       "       -0.6225406 , -0.54045251, -0.45285485, -0.36064061, -0.26474988,\n",
       "       -0.16616018, -0.06587659,  0.03507857,  0.13567613,  0.23489055,\n",
       "        0.33171042,  0.4251487 ,  0.51425287,  0.59811455,  0.67587883,\n",
       "        0.74675295,  0.8100144 ,  0.86501827,  0.91120382,  0.94810022,\n",
       "        0.97533134,  0.99261957,  0.99978867,  0.99676556,  0.98358105,\n",
       "        0.96036956,  0.9273677 ,  0.88491192,  0.83343502,  0.77346177,\n",
       "        0.70560358,  0.63055219,  0.54907273,  0.46199582,  0.37020915,\n",
       "        0.27464844,  0.17628785,  0.07613012, -0.0248037 , -0.12548467,\n",
       "       -0.2248864 , -0.32199555, -0.41582217, -0.50540974, -0.58984498,\n",
       "       -0.66826712, -0.7398767 , -0.8039437 , -0.859815  , -0.90692104,\n",
       "       -0.94478159, -0.97301068, -0.99132055, -0.99952453, -0.99753899,\n",
       "       -0.98538417, -0.96318398, -0.93116473, -0.88965286, -0.83907153])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2=np.cos(x)\n",
    "y2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,label='sin(x)')\n",
    "plt.plot(x,y2,label='cos(x)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,label='sin(x)')\n",
    "plt.plot(x,y2,label='cos(x)')\n",
    "plt.legend()\n",
    "plt.axis([-5,15,-2,2])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 绘制柱状图和饼图\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=['Q1','Q2','Q3','Q4']\n",
    "y=[10,30,20,60]\n",
    "plt.bar(x,y,color='g',width=0.3)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(x,y,color='g',width=0.3)\n",
    "plt.grid(True)\n",
    "plt.text(0.2,50,'test')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rect = plt.bar(x,y,color='g',width=0.3)\n",
    "plt.grid(True)\n",
    "\n",
    "\n",
    "#绘制标度值\n",
    "for index, item in enumerate(rect):\n",
    "    _x = item.get_x()+0.1\n",
    "    _y = item.get_height()\n",
    "    plt.text(_x,_y,y[index])\n",
    "    \n",
    "plt.ylim(0,70)    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 饼图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Q1', 'Q2', 'Q3', 'Q4']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10, 30, 20, 60]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(y,labels=x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.axes(aspect=1)\n",
    "plt.pie(y,labels=x,autopct='%2.f%%',explode=[0.2,0,0,0],shadow=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "284"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(100)\n",
    "data = np.random.normal(9000,3000,size=300)\n",
    "data = data[data>=4000]\n",
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data,color='g',rwidth=0.6,alpha=0.6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data,color='g',rwidth=0.6,alpha=0.6,bins=20) #bins代表柱子个数\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 箱线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=np.arange(1,10)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=np.concatenate([data,[4,7,8,9,-4]])\n",
    "plt.boxplot(data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#whis:指定上下边界四分位距离，默认1.5倍四分位差\n",
    "plt.boxplot(data,whis=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data,showmeans=True,meanline=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 子图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(3,1,1)\n",
    "plt.subplot(3,1,2)\n",
    "plt.subplot(3,1,3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,2,1)\n",
    "plt.subplot(2,2,2)\n",
    "plt.subplot(2,2,3)\n",
    "plt.subplot(2,2,4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x243e220d448>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,2,1)\n",
    "plt.subplot(2,2,2)\n",
    "plt.subplot(2,1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] File ./salay.txt does not exist: './salay.txt'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-49-160318ca8d1a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'./salay.txt'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'\\t'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\justin.zou\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    674\u001b[0m         )\n\u001b[0;32m    675\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 676\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    678\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\justin.zou\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    446\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    447\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 448\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    449\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    450\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\justin.zou\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    878\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    879\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 880\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    881\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    882\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\justin.zou\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1112\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"c\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1113\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"c\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1114\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1115\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1116\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"python\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\justin.zou\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1889\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"usecols\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1890\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1891\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1892\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1893\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] File ./salay.txt does not exist: './salay.txt'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_table('./salay.txt',sep='\\t')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rc('font',**{'family':'Microsoft YaHei,SimHei'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.subplot(2,2,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.subplot(2,2,1)\n",
    "plt.plot(data['城市'],data['月均工资'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.index=data['城市']\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.set_index('城市')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.subplot(2,2,1)\n",
    "plt.bar(data.index,data['月均工资'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[['月均工资']].boxplot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[['月均工资']].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.subplot(2,2,1)\n",
    "plt.plot(data.index,data['月均工资'])\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt.bar(data.index,data['月均工资'])\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.bar(data.index,data['月均工资'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "data= pd.DataFrame(np.random.rand(10,4),columns=('A','B','C',\"D\"))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data.plot(kind='barh',stacked=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.savefig('./test.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./log.txt',header=None,sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns=['id','api','count','res_time_sum',\"res_time_min\",\"res_time_max\",\"res_time_avg\",\"interval\",\"created\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sample(5) #随机采样多次执行，数据不一样，看大概"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.info() #查看内存占用空间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['api'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop('api',axis=1) #优化内存，指定axis,指定删除一列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['created'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[df.created == '2019-05-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[(df.created >= '2019-05-01') & (df.created <= '2019-05-02')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index  #查看当前索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index=df['created']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['2019-05-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = pd.to_datetime(df.created)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['2019-05-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2 fizz 4 buzz fizz 7 8 fizz buzz 11 fizz 13 14 fizzbuzz 16 17 fizz 19 buzz fizz 22 23 fizz buzz 26 fizz 28 29 fizzbuzz 31 32 fizz 34 buzz fizz 37 38 fizz buzz 41 fizz 43 44 fizzbuzz 46 47 fizz 49 buzz fizz 52 53 fizz buzz 56 fizz 58 59 fizzbuzz 61 62 fizz 64 buzz fizz 67 68 fizz buzz 71 fizz 73 74 fizzbuzz 76 77 fizz 79 buzz fizz 82 83 fizz buzz 86 fizz 88 89 fizzbuzz 91 92 fizz 94 buzz fizz 97 98 fizz buzz\n"
     ]
    }
   ],
   "source": [
    "res = []\n",
    "for i in range(1, 101):\n",
    "    if i % 15 == 0:\n",
    "        res.append('fizzbuzz')\n",
    "    elif i % 3 == 0:\n",
    "        res.append('fizz')\n",
    "    elif i % 5 == 0:\n",
    "        res.append('buzz')\n",
    "    else:\n",
    "        res.append(str(i))\n",
    "print(' '.join(res))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
